Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what people ask of place, and how often - remains poorly characterised at scale. We apply dense sentence embeddings, a lightweight SetFit classifier, and density-based clustering to the full MS MARCO corpus of 1.01 million real Bing queries without prior filtering for toponyms or spatial keywords, identifying 181,827 geospatial queries (18.0%), nearly threefold the 6.17% labelled as Location in the original annotations. The resulting taxonomy of 88 query categories reveals that geospatial web search is dominated by transactional and practical lookups: costs and prices alone account for 15.3% of geospatial queries, nearly twice the size of the entire physical geography theme. Much of this activity - costs, opening hours, contact details, weather, travel recommendations - falls outside the scope of what traditional GIS and knowledge graphs are built to serve. The categories vary substantially in the kind of answer they admit, from deterministic lookups answerable from spatial databases or knowledge graphs to evaluative or temporally volatile queries that require generative or real-time systems. We discuss implications for hybrid retrieval architectures and for benchmarks of geographic reasoning in large language models. We openly release the labelled dataset, classifier, and taxonomy.

翻译：网络搜索查询中涉及地点的比例远超现有标注方案所揭示的程度，但关于地理空间网络搜索查询的图景——即人们对地点提出的问题及其频率——至今仍未在大规模数据中得到充分刻画。我们采用密集句子嵌入方法、轻量级SetFit分类器以及基于密度的聚类技术，对包含101万条真实必应查询的完整MS MARCO语料库进行了分析，且未事先对地名或空间关键词进行过滤。由此识别出181,827条地理空间查询（占18.0%），这一比例几乎是原始标注中“位置”标签（6.17%）的三倍。由此产生的包含88个查询类别的分类体系揭示出，地理空间网络搜索以交易性和实用性查询为主：仅成本与价格类查询就占到地理空间查询的15.3%，其规模接近整个自然地理主题的两倍。这些活动中的大部分——如成本、营业时间、联系方式、天气、旅行推荐——超出了传统地理信息系统和知识图谱所能服务的范围。各类查询在所需答案的类型上存在显著差异，从可通过空间数据库或知识图谱回答的确定性查找，到需要生成式或实时系统处理的评价性或时效性不稳定的查询。我们讨论了这些发现对混合检索架构以及大型语言模型地理推理基准测试的启示。我们还公开发布了标注数据集、分类器及分类体系。